Tagged in Machine Learning
Machine learning is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence
Machine Learning Reproducibility: A Kaggle Competition Use-Case
16 December, 2020
Even though Reproducibility in Machine Learning is a theme that people hear about now and then, we still see that people are practicing it only to a certain degree. Even between Kaggle [https://www.kaggle.com/] competition winners, we still see a lot of hard-to-reproduce code in Notebooks. Our goal here is to outline some reproducibility elements and how we tackled them in a recent competition. First, what reproducibility stands for in Machine Learning? During a Machine Learning project, we hav
The path to putting your ML model in production
24 November, 2020
Suppose you are a Data Scientist or Machine Learning Engineer (or another role name of this kind). You took your time to analyze your dataset, clean it, and prepare it to train your model. You then prepared many model candidates using the most recent techniques and took your time to fine-tune them. After all this extensive work, you finally created a model to be proud of. You finally finished your job. Well, unfortunately, not. If your model never goes live and is actively used, delivering value
Deep Learning and the fear of frauds
09 November, 2020
Soon we might live in a world where one can never be sure that video and voice recording is real, no matter how realistic it looks and sounds. Deep learning methods are used with artificial neural networks to create what is known as deepfakes – visual and audio content that, to the naked eye, looks absolutely real. The potential uses of deepfakes are limited only by the imagination of people who have access to the technology required to manufacture them. As technology advances, the tools for cr
Ranking labs-of-origin for genetically engineered DNA using Metric Learning
23 October, 2020
With the constant advancements of genetic engineering, a common concern is to be able to identify the lab-of-origin of genetically engineered DNA sequences. For that reason, AltLabs has hosted the Genetic Engineering Attribution Challenge to gather many teams to propose new tools to solve this problem. Here we show our proposed method that aims to rank the most likely labs-of-origin and generate embeddings for DNA sequences and labs. These embeddings can also be used to perform various other tas
7 common mistakes of a machine learning beginner
15 October, 2020
In recent years, the term Artificial Intelligence has gained strength and together with it have emerged some professions such as Data Scientist and Machine Learning Engineer. Knowing and applying machine learning is attractive and appears to be the path to success. However this path can be troubled and especially discouraging for those who are just starting out. Over the years working as a Data Scientist and Machine Learning Researcher, I have witnessed several common mistakes that made life di
AI Infrastructure Alternatives for your Business
13 October, 2020
With cloud offerings becoming more abundant and diverse, cloud infrastructure seems to offer a much cheaper and simpler alternative to an on-premises data center. Many organizations, that need Artificial Intelligence to help with decision-making, problem-solving, etc. face a complicated decision: what is the best infrastructure deployment for AI workloads? Generally speaking, there are three possible deployment options. You can run your AI on-premises in your own datacenter, rent some space at
What Kind of AI Infrastructure is Best for my Business?
30 September, 2020
In this week's Exponential Chats, some of the team members responsible for Amalgam's development will have a chat about the various infrastructure alternatives available when it comes to training and deployment of Artificial Intelligence models. Between Cloud, Colocation, and On-Premise which one would you say is the best infrastructure for your AI needs? Come join us and participate by asking questions or giving your opinion in the live chat. - Adriano Marques is the founder and CEO of Exponen
The Eight Challenges You'll Face With On-Premise Artificial Intelligence
18 September, 2020
As glamorous as it is to have your own Artificial Intelligence Optimized On-Premise Data Center, it doesn't come easy. It is absolutelly true that if done right it boasts much better performance and much lower costs than resorting to the cloud or even using co-location to perform your processing workload when creating AI driven solutions. However, most people are not aware of what really makes an AI Optimized Data Center and end up building an expensive half-baked solution that can't perform o
How safe are self-driving cars?
09 September, 2020
In this week's Exponential Chats, some of the team members responsible for Amalgam's development will have a chat about the safety of current self-driving technology. Despite the fact that there are very few cars on the road today capable of self-driving in all conditions, most new cars are already incorporating some of that technology and soon we'll be trusting our lives to it. Whether or not you're ready give up on your steering wheel we think that you'll enjoy this discussion. Come join us an
A few potential uses (and misuses) for GPT-3
11 August, 2020
GPT-3 is a 175 billion parameter autoregressive language model by OpenAI released in May 2020. It is a deep learning system that takes input in the form of human readable language and produces human readable output. The OpenAI team tested GPT-3 in Few-shot learning mode. The model is given a verbal description of the task and a few examples of context and completion at inference time. Then the machine is given another instance of context and expected to provide the completion. Two variations of
GPT-3: Is this the beginning of Skynet?
05 August, 2020
In this week's Exponential Chats, some of the team members responsible for Amalgam's development will have a chat about Open AI's recently announced language model GPT-3.
Top 6 Engineering Challenges for Machine Learning
12 June, 2020
It is already 2020, and on top of a pandemic and civil unrest, our Machine Learning community still has to face challenges when working their way through creating models to solve the world's problems.